Integrated ACMG approved genes and ICD codes for the translational research and precision medicine

Timely understanding of biological secrets of complex diseases will ultimately benefit millions of individuals by reducing the high risks for mortality and improving the quality of life with personalized diagnoses and treatments. Due to the advancements in sequencing technologies and reduced cost, genomics data is developing at an unmatched pace and levels to foster translational research and precision medicine. Over ten million genomics datasets have been produced and publicly shared in the year 2022. Diverse and high-volume genomics and clinical data have the potential to broaden the scope of biological discoveries and insights by extracting, analyzing, and interpreting the hidden information. However, the current and still unresolved challenges include the integration of genomic profiles of the patients with their medical records. The disease definition in genomics medicine is simplified, when in the clinical world, diseases are classified, identified, and adopted with their International Classification of Diseases (ICD) codes, which are maintained by the World Health Organization (WHO). Several biological databases have been produced, which includes information about human genes and related diseases. However, still, there is no database exists, which can precisely link clinical codes with relevant genes and variants to support genomic and clinical data integration for clinical and translation medicine. In this project, we are focused on the development of an annotated gene-disease-code database, which is accessible through an online, cross-platform, and user-friendly application i.e., PAS-GDC. However, our scope is limited to the integration of ICD-9 and ICD-10 codes with the list of genes approved by the American College of Medical Genetics and Genomics (ACMG). Results include over seventeen thousand diseases and four thousand ICD codes, and over eleven thousand gene-disease-code combinations. ### Competing Interest Statement The authors have declared no competing interest. * (ACMG) : American College of Medical Genetics and Genomics (ASCII) : American Standard Code for Information Interchange (CSS) : Cascading Style Sheets (DNA) : Deoxyribose Nucleic Acid (EHR) : Electronic Health Records (FDA) : Food and Drug Administration (FAIR) : Findable, Accessible, Interactive, and Reusable (GVViZ) : Visualizing Genes with disease causing Variants (GUI) : Graphical User Interface (HTML) : Hypertext Markup Language (ICD) : International Classifications of Disease (IDE) : Integrated Development Environment (NGS) : Next Generation Sequencing (NDC) : National Drug Code (PAS) : PROMIS-APP-SUITE (GDC) : Gene Disease Code (SAM) : Sequence Alignment Map (SQL) : Structured Query Language (VCF) : Variant Call Format (WHO) : World Health Organization (WGS) : Whole Genome Sequencing (WES) : Whole Exome Sequencing